The only thing that matters is if LLMs with sufficient scaling can become frontier AI researchers kicking off the exponential. Everything else is transient noise.
I agree with your sentiment (about the noise), however I think this over simplifies it a bit. We may get AI that is super-human at frontier research and dramatically accelerates the pace, and still have to wait decades before it disrupts the job market (or maybe never displaces all work).
For one, the answer may depend on material science and chip manufacturing that can take a very long to build out a supply chain for even with super AI help.
And we may just find that the human mind is way more capable than we thought and even with accelerating research it's just a harder problem than anyone expected, even algorithmically.
I expect it to be a bit of both, and from ~2015 - 2025 I was in the "AI is coming for all our jobs" camp. My perspective changed last year after doing a deep dive into latest science on the human brain. (I've kept a very close eye on AI dev progress for 12+ years.
I don't see why that's the case when you have super-human researchers on tap. There are indeed physical (supply chain-y) issues to deal with but isn't the whole point that: 1. Super-human at AI research + scaling to millions of instances will probably result in super-intelligence in everything which is not AI research. (a subset of which is white-collar work) 2. Use that super-intelligence to solve any supply-chain issues you might be facing.
> And we may just find that the human mind is way more capable than we thought and even with accelerating research it's just a harder problem than anyone expected, even algorithmically.
I hope so but whenever I do, I feel like I'm coping hard and not dealing with the facts.
I'm not saying we're there yet - I'm saying the trend lines are clear.
What if the answer is flatly: no? All that other stuff starts to matter a lot then.
Predicating your business decisions on a potential breakthrough that may never come is frankly insane. Imagine if at the dawn of the car industry Ford decided that it's actually a race to build the first flying car and nothing else matters.
I think we know the answer to that already - LLMs show no sign of improving intelligence and instead providers are going down the ‘agentic’ rabbit hole.
There are too many things missing, like a world model, understanding, and taste (in the sense of knowing what is good and what is not good).
I'm not sure where you're getting this. I don't work at Anthropic but Fable (Mythos) seems demonstrably smarter than Opus for pretty much any definition of smarter and they claim that Opus was used heavily in Mythos development (yeah I know take this with a massive pinch of salt).
Either way if the models are indeed helping development, even on the engineering, you can iterate on models faster and even if they're not contributing to core research yet you still have a baby exponential by improving the engineering.
Took that nonsense to Capitol Hill, trying to tell a bunch of politicians who knew damn well they are only there as long as they can keep their voters employed. They could have asked their own AI what happens when employment reaches 40-50%. Hint: it's never good. They were going to become another problem the government had to solve.
Also, UBI is non-starter no matter what Sam Altman believes.
As long as the term “AI” means by-and-large LLMs with additional features sprinkled on top, the answer is no. More likely (without careful vetting by the folks aggregating these models) is that the quality will go down as more and more AI-generated output gets subsumed into these models.
Even without that particular problem, LLMs-as-AI can only give us probabilistic outputs based on inputs; and by definition they’re reliant on humans to provide the training data for their model. Without specialized knowledge or training on that knowledge (And even with it, viz. Meta’s engineering), we don’t have to worry about AI itself. We do have to worry what investors who are looking for outsized returns will do to get those returns, job market be damned.
The problem for us isn’t that AI will take our jobs; it’s that snake-oil salesmen can sell the idea that AI will take our jobs, investors buy into it, companies try it, fire their folks, the snake-oil salesmen IPOs, the companies that bought into this idea implode in some form or fashion, and the salesmen have already taken the money and ran. Of course, we still lose our jobs, but maybe (!) we get them back when this all fails?
So I don’t see accuracy declining at least for programming.
How do those chat bots discern that the ‘web searches’ they’re using are returning human generated information only that’s been vetted instead of LLM output?
If you believe AI will 10x you're developers you've drunk the kool-aid, if you believe AI will have no impact on your developers then you're being stubbornly ignorant.
Until AI no longer needs human supervision, it's more profitable to tax as many employees as possible.
If that makes me a bad person, fine. If a few CEO's wind up working at 7-11 to make rent money, all the better.
https://econlab.substack.com/p/we-can-finally-say-ai-isnt-ki...
Most, if not nearly all, of these teams have little to show ROI wise and the music on the AI bubble is slowing dramatically. They went from seemingly unlimited budgets and headcount when CEOs said “get me some of that AI” to some really uncomfortable scenes playing out know as the same CEOs realize this has cost a fortune with little to show for it.
there is an oversupply of SWE and a dwindling supply of jobs which pushes wages down
article misses an important point that these big tech companies are all listed on the public market, any narrative about their decisions should weigh that reality and why suddenly its being disseminated.
Welcome to the postmodern internet. It's vibes all the way down.
This assumes that there aren't algorithmic breakthroughs which reduce training/inference costs by several OOMs.
How much do these models need to do before people throw their hands in the air and say, ok this is happening. The Erdos unit distance problem, which as far as I understand was approached by multiple competent mathematicians was solved by a frontier model. Sure people argue there was no novelty there (I cannot comment as a non-mathematician) but it feels like they can draw lines laterally from deep knowledge in different fields (in this case combinatorics and algebraic number theory I believe) and solve problems.
Now if you have millions of instances running in parallel, all "probabilistic", working on frontier AI research I really don't see the blocker (and believe me I wish I did).
Open AI et al are hemorrhaging absurd amounts of money. It's not clear whether there will ever be a good balance between cost, value, and price.
Lots of companies are already questioning the value they get from LLMs at current prices which are obviously not enough to generate profits.
openquery•1h ago